Book Image

Reinforcement Learning with TensorFlow

By : Sayon Dutta
Book Image

Reinforcement Learning with TensorFlow

By: Sayon Dutta

Overview of this book

Reinforcement learning (RL) allows you to develop smart, quick and self-learning systems in your business surroundings. It's an effective method for training learning agents and solving a variety of problems in Artificial Intelligence - from games, self-driving cars and robots, to enterprise applications such as data center energy saving (cooling data centers) and smart warehousing solutions. The book covers major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You'll also be introduced to the concept of reinforcement learning, its advantages and the reasons why it's gaining so much popularity. You'll explore MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, and temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have gained a firm understanding of what reinforcement learning is and understand how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.
Table of Contents (21 chapters)
Title Page
Packt Upsell

Asynchronous advantage actor critic

In the architecture of asynchronous advantage actor-critic, each learning agent contains an actor-critic learner that combines the benefits of both value- and policy-based methods. The actor network takes in the state as input and predicts the best action of that state, while the critic network takes in the state and action as the inputs and outputs the action score to quantify how good the action is for that state. The actor network updates its weight parameters using policy gradients, while the critic network updates its weight parameters using TD(0), in other words, the difference of value estimates between two time steps, as discussed in Chapter 4Policy Gradients.

In Chapter 4Policy Gradients, we studied how updating the policy gradients by subtracting a baseline function from the expected future rewards in the policy gradients reduces the variance without affecting the expectation value of the gradient. The difference between the expected future...